Identifying and modeling factors that influence typology has been one of the most contested issues in phonology with two major lines of thought emerging in this discussion: the Analytic Bias (AB) and Channel Bias (CB) approaches (Moreton 2008). Empirical evidence in favor of both approaches exists, yet very few attempts have been made to model them together. This paper aims to fill this gap and proposes a new MaxEnt-compatible model of phonological typology that models both AB and CB together. The first step towards a new model of typology is to establish quantitative models of each of the subcomponents: AB and CB. To encode the AB portion of the typology, we adopt Wilson's (2006) approach of differentiating variance in the prior of a MaxEnt model of phonological learning; to encode the CB portion, we adopt Beguš's (2016) new model of typology within CB that operates with Historical Probabilities of Alternations and an estimation method called Bootstrapping Sound Changes. This paper proposes a new model of typology that combines differentiating prior variance (AB; Wilson 2006) with estimating Historical Weights based on Historical Probabilities (CB; Beguš 2016), whereby both variables influence the typology. I further argue that this new model performs better than the current "split" models on the basis of two alternations, post-nasal voicing and devoicing, and point to future directions this line of research should take.